Objective To investigate the clinical safety and efficacy of predictive extracorporeal membrane oxygenation (ECMO) support during liver transplantation in high-risk recipients. Methods A single-center retrospective study was conducted. A total of 35 liver transplant recipients who underwent predictive ECMO assistance during surgery at the Second Affiliated Hospital of Guangxi Medical University from January 2021 to April 2025 were included. Baseline characteristics, ECMO application, ECMO operating parameters and hemodynamic conditions during ECMO operation were analyzed. The cumulative survival rate was calculated using the Kaplan-Meier method and survival curves were plotted. Results Veno-arterial ECMO in 35 recipients was established after induction of general anesthesia. The median ECMO operation time was 8.5 h, and the success rate of weaning from ECMO was 97%. The cardiac index (CI) during the non-liver period and the new liver period was higher than that before cannulation, and CI increased during the non-liver period and before weaning (all P<0.001). The mean invasive arterial pressure and central venous pressure remained stable. No ECMO-related complications occurred in the recipients. The average survival time was (25 ± 3) months. The main causes of death were severe pulmonary infection and multiple organ failure. Conclusions For liver transplant recipients with severe cardiopulmonary dysfunction, hemodynamic disorders, or multiple organ failure risk factors, implementing predictive ECMO support during liver transplantation through active risk prediction is a safe and effective circulatory guarantee strategy.

OBJECTIVES: To investigate the characteristics of immune cell infiltration in tumor samples from Chinese patients with clear cell renal cell carcinoma (ccRCC) and the correlation of immune cell infiltration with tumor stage and response to immunotherapy. METHODS: Tumor samples and clinicopathological data were collected from 154 ccRCC patients treated in Nanfang Hospital, Southern Medical University from October, 2020 to October, 2023. The immune cell types infiltrating the tumor tissues were identified using immunohistochemistry and immunofluorescence staining, and their correlations with the patients' clinicopathological characteristics were analyzed. Patient-derived tumor tissue fragment models (PDTF) models, constructed using tumor tissues from 22 patients, were treated with PD-1 monoclonal antibody, and T cell activation was detected using flow cytometry to assess the patients' responses to immunotherapy. RESULTS: In Chinese ccRCC patients included in this study, CD8⁺ T cells, CD4⁺ T cells, and CD3⁺ T cells were the most abundant in the tumor tissues. Higher infiltration levels of CD3⁺ T cells (P=0.004), PD-1⁺ T cells (P=0.020), CD68⁺ T cells (P=0.049), CD79⁺ T cells (P=0.049), and Tryptase⁺ cells (P=0.049) were all positively correlated with a larger tumor size (≥5 cm). A higher infiltration level of CD4⁺ T cells was associated with a lower tumor stage. Patients with higher International Society of Urological Pathology (ISUP) grades had higher infiltration levels of CD3⁺ T cells (P=0.023), CD8⁺ T cells (P=0.045), PD-1⁺ T cells (P=0.014), CD20⁺ B cells (P=0.020) and CD79⁺ B cells (P=0.049), and lower levels of Tryptase⁺ cells (P=0.001). Patients with abundant infiltrating immune cells tended to have better responses to immunotherapy. CONCLUSIONS The infiltrating immune cells are heterogeneous in Chinese ccRCC patients, and immune cell infiltration characteristics are closely correlated with clinicopathological parameters of the patients.
Humans ; Carcinoma, Renal Cell/pathology* ; Kidney Neoplasms/pathology* ; Immunotherapy ; Male ; Lymphocytes, Tumor-Infiltrating/immunology* ; Female ; Middle Aged ; CD8-Positive T-Lymphocytes/immunology* ; Aged ; T-Lymphocytes/immunology* ; Programmed Cell Death 1 Receptor/immunology* ; Adult ; CD4-Positive T-Lymphocytes/immunology* ; Neoplasm Staging

Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) is a complex disease that is often accompanied by mental health disorders. However, the potential mechanisms underlying the heterogeneous clinical presentation of CP/CPPS remain uncertain. This study analyzed widely targeted metabolomic data of expressed prostatic secretions (EPS) and plasma to reveal the underlying pathological mechanisms of CP/CPPS. A total of 24 CP/CPPS patients from The Second Nanning People's Hospital (Nanning, China), and 35 asymptomatic control individuals from First Affiliated Hospital of Guangxi Medical University (Nanning, China) were enrolled. The indicators related to CP/CPPS and psychiatric symptoms were recorded. Differential analysis, coexpression network analysis, and correlation analysis were performed to identify metabolites that were specifically altered in patients and associated with various phenotypes of CP/CPPS. The crucial links between EPS and plasma were further investigated. The metabolomic data of EPS from CP/CPPS patients were significantly different from those from control individuals. Pathway analysis revealed dysregulation of amino acid metabolism, lipid metabolism, and the citrate cycle in EPS. The tryptophan metabolic pathway was found to be the most significantly altered pathway associated with distinct CP/CPPS phenotypes. Moreover, the dysregulation of tryptophan and tyrosine metabolism and elevation of oxidative stress-related metabolites in plasma were found to effectively elucidate the development of depression in CP/CPPS. Overall, metabolomic alterations in the EPS and plasma of patients were primarily associated with oxidative damage, energy metabolism abnormalities, neurological impairment, and immune dysregulation. These alterations may be associated with chronic pain, voiding symptoms, reduced fertility, and depression in CP/CPPS. This study provides a local-global perspective for understanding the pathological mechanisms of CP/CPPS and offers potential diagnostic and therapeutic targets.
Humans ; Male ; Prostatitis/blood* ; Adult ; Pelvic Pain/blood* ; Metabolomics ; Prostate/metabolism* ; Middle Aged ; Chronic Pain/blood* ; Metabolome ; Case-Control Studies ; Tryptophan/blood* ; Depression/blood* ; Oxidative Stress/physiology* ; Chronic Disease ; Lipid Metabolism/physiology*

Nonobstructive azoospermia (NOA), one of the most severe types of male infertility, etiology often remains unclear in most cases. Therefore, this study aimed to detect four biallelic detrimental variants (0.5%) in the minichromosome maintenance domain containing 2 ( MCMDC2 ) genes in 768 NOA patients by whole-exome sequencing (WES). Hematoxylin and eosin (H&E) demonstrated that MCMDC2 deleterious variants caused meiotic arrest in three patients (c.1360G>T, c.1956G>T, and c.685C>T) and hypospermatogenesis in one patient (c.94G>T), as further confirmed through immunofluorescence (IF) staining. The single-cell RNA sequencing data indicated that MCMDC2 was substantially expressed during spermatogenesis. The variants were confirmed as deleterious and responsible for patient infertility through bioinformatics and in vitro experimental analyses. The results revealed four MCMDC2 variants related to NOA, which contributes to the current perception of the function of MCMDC2 in male fertility and presents new perspectives on the genetic etiology of NOA.
Humans ; Male ; Azoospermia/genetics* ; Meiosis/genetics* ; Spermatogenesis/genetics* ; Adult ; Exome Sequencing ; Microtubule-Associated Proteins/genetics* ; Alleles ; Infertility, Male/genetics*

OBJECTIVE: Automatic planning is a commonly used alternative to manual planning. This study evaluated the clinical performance of automatic plans available in commercial treatment planning systems for nasopharyngeal carcinoma (NPC) treatment by comparing automatic planning with manual planning. METHODS: A total of 14 patients with nasopharyngeal carcinoma were enrolled in the study. For each patient, three different sets of clinical goals were used to generate three hybrid automatic plans based on 3D dose distribution prediction and three automatic plans based on script, respectively, which were compared with the manual plans used in clinic. RESULTS: The dose coverage performance of the automatic planning based on 3D dose distribution prediction on the planning target volume (PTV) was comparable to that of the manual planning. Automatic planning based on 3D dose prediction achieved the level of manual planning in most organs at risk. However, automatic planning based on scripts did not perform well in the prediction of some organs at risk, especially the parotid gland. CONCLUSION The hybrid automatic plan based on 3D dose distribution prediction can reach the level of manual planning and have good robustness with the change of clinical objective.
Humans ; Nasopharyngeal Neoplasms/radiotherapy* ; Radiotherapy Planning, Computer-Assisted/methods* ; Nasopharyngeal Carcinoma ; Male ; Female ; Middle Aged ; Adult ; Carcinoma ; Radiotherapy Dosage

ObjectiveTo investigate the effects of Scutellariae Radix combined with epidermal growth factor receptor-tyrosine kinase inhibitors （EGFR-TKIs） on cell proliferation， apoptosis， cancer stem cell （CSC） marker expression， and metabolism in non-small cell lung cancer （NSCLC） cells. MethodsThe anti-tumor effects of Scutellariae Radix and EGFR-TKIs （gefitinib or osimertinib） in NSCLC cells were evaluated using the cell counting kit-8 （CCK-8） and Annexin V-FITC/propidium iodide （PI） double staining apoptosis assay. The activity of Scutellariae Radix and EGFR-TKIs in three-dimensional （3D） cultures of NSCLC cells was assessed using the CellTiter-Glo® 3D cell viability assay. The mRNA and protein expression levels of CSC markers， sex determining region y box protein 2 （SOX2） and aldehyde dehydrogenase 1 family member A1 （ALDH1A1）， were detected by quantitative real-time polymerase chain reaction （Real-time PCR） and Western blot， respectively. Changes in intracellular reactive oxygen species （ROS） levels were detected by ROS staining， and the redox ratio was detected by femtosecond laser labeling free imaging （FLI）. ResultsUnder both two-dimensional （2D） and 3D culture conditions， compared with the blank group and EGFR-TKI group， the combination group showed significantly reduced cell viability and increased apoptosis rate （P<0.05）. Compared with the EGFR-TKI group， the mRNA and protein levels of CSC markers were significantly downregulated in the combination group （P<0.05）. Additionally， the redox ratio was significantly elevated （P<0.05）， and ROS levels were also increased in the combination group compared with the EGFR-TKI group. ConclusionIn NSCLC cells， Scutellariae Radix enhances the redox ratio and increases ROS levels， thereby inhibiting the expression of CSC markers and strengthening the anti-tumor effects of EGFR-TKIs. This provides a novel molecular mechanism by which Scutellariae Radix may enhance the sensitivity of targeted therapies.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

The gut-brain axis is a bidirectional communication pathway connecting the central nervous system and gastrointestinal tract,playing a key role in the occurrence and development of diseases related to this axis.The vermiform appendix,as a part of the gut that is connected to the cecum,has a unique anatomical location,a rich microbiome,and abundant immune cells.Appendicitis and appendectomy have been found to be associated with the development of diseases related to the gut-brain axis.This review first introduces the anatomy and functions of the vermiform appendix and then expounds the associations of appendicitis and appendectomy with diseases related to the gut-brain axis.Furthermore,this review summarizes and prospects the mechanisms of the vermiform appendix in affecting the occurrence and development of diseases related to the gut-brain axis.
Humans ; Appendix/anatomy & histology* ; Brain ; Appendicitis ; Appendectomy/adverse effects* ; Gastrointestinal Microbiome ; Brain-Gut Axis

OBJECTIVE To explore the characteristics and influencing factors of immune-related adverse events （irAEs） induced by tislelizumab in patients with liver cancer. METHODS A retrospective cohort of 203 liver cancer patients treated with tislelizumab in Guangxi Medical University Cancer Hospital from May 2022 to March 2024 was included. These patients were divided into an irAEs group （58 cases） and a non-irAEs group （145 cases）. Clinical data were collected and compared between the two groups. A multivariate logistic regression model was employed to analyze factors influencing the occurrence of irAEs and establish a predictive model. The receiver operator characteristic （ROC） curve was plotted to evaluate the predictive value of the model for the occurrence of irAEs. The correlation between irAEs and overall survival （OS） as well as progression free survival （PFS） in patients was analyzed using the Kaplan-Meier method. RESULTS The irAEs induced by tislelizumab in liver cancer patients were predominantly grade 1-2 （89.71%）， mainly manifesting as hematological toxicity （42.65%） and hepatotoxicity （20.59%）， and mostly occurred within 1-12 cycles after tislelizumab treatment. Compared with liver cancer patients without underlying liver diseases， those with chronic hepatitis B had a higher incidence of irAEs. Statistically significant differences were observed between the irAEs and non-irAEs groups in terms of the number of patients with a China Liver Cancer Staging （CNLC） stage ≥Ⅱ， white blood cell count， neutrophil count， systemic immune-inflammation index （SII）， and neutrophil-to-lymphocyte ratio （NLR） （P＜0.05）. Multivariate Logistic regression analysis revealed that CNLC stage ≥Ⅱ was an independent risk factor for the occurrence of irAEs （P＝0.027）. The ROC curve indicated that neutrophil count， white blood cell count， NLR， and SII all demonstrated certain predictive potential for the occurrence of irAEs （with area under the curve values of 0.614， 0.592，0.591， and 0.589， respectively）. The Kaplan-Meier survival curve showed no statistically significant differences in PFS and OS between the irAEs and non-irAEs groups， among patients with different grades of irAEs， and among irAEs patients with different CNLC stages （P＞0.05）. CONCLUSION The irAEs induced by tislelizumab in liver cancer patients are relatively mild （grade 1-2），mainly manifesting as hematological toxicity and hepatotoxicity. Liver cancer patients with concurrent chronic hepatitis B are at a higher risk of developing irAEs. CNLC stage ≥Ⅱ is an independent risk factor for irAEs induced by tislelizumab. Neutrophil count， white blood cell count， NLR， and SII have certain predictive value for the occurrence of irAEs.

Objective: To explore the characteristic of obesity in adolescents with major depressive disorder and its relationship with inflammatory cytokines. Methods : One hundred and forty adolescents with major depressive disorder were enrolled. According to the classification standard of body mass index(BMI) for adolescents in China, the patients were classified into underweight group, normal group, overweight group and obese group. The center for epidemiologic studies depression scale(CES-D) was used to evaluate symptoms of depression in patients, and ultrasensitive multiplex electrochemiluminescence detection technology was used to measure the levels of plasma inflammatory cytokines including interleukin(IL)-6,IL-17A,IL-1β,IL-6,IL-8 and tumour necrosis factor(TNF)-α. One-way ANOVA or Kruskal-WallisHtest and chi-square test were used for comparison between groups. Binary Logistic regression was used to analyze the influencing factors of obesity in adolescent patients with major depressive disorder. Results : Among the 140 adolescent patients with major depressive disorder, wasting were 9.3%(13/140), overweight were 17.9%(25/140) and obesity were 6.4%(9/140) respectively. There were statistically significant differences in gender(χ2=8.301,P<0.05) and inflammatory cytokines IL-6(H=16.217,P<0.01), IL-8(H=10.926,P<0.05) and TNF-α(H=7.879,P<0.05) among the four groups. Analysis of covariance showed that the difference in levels of the inflammatory cytokine IL-6(F=4.486,P<0.01) remained statistically significant after controlling for age, gender and antidepressant use. The results of multiple comparisons showed that compared with the wasting group, the plasma IL-6(Z=-3.843,PBonferroni calibrate<0.01) were higher in the obese group; compared with the normal group, the obesity rate of males was higher than that of females(χ2=8.812,PBonferroni calibrate<0.01), and the level of IL-6 in the obese group(Z=-3.023,PBonferroni calibrate<0.05) was higher. Binary Logistic regression analysis showed that plasma IL-6(OR=2.500,P<0.01) and gender(OR=11.292,P<0.01) were independent influencing factors for obesity in patients with adolescent depressive disorders. Conclusion There are gender differences in obesity rates in adolescents with depressive disorders, and obesity is associated with elevated levels of inflammatory cytokines.